This letter presents an experiment for estimating accurate state in distributed power systems. This letter employs a technique that combines a projected Jacobi method with a parallel dual-type method to solve the distributed state estimation with constraints problems. Via numerous tests, this letter demonstrates the efficiency of the proposed method on the IEEE 118-bus with four subsystems in a PC network.

This work presents a two-stage ordinal optimization theory-based approach for solving the throughput maximization problems with power constraints of sub-carrier assignment and power allocation in multi-user orthogonal frequency division multiplexing uplink systems. In the first stage, a crude but efficient model is employed to evaluate the performance of a sub-carrier assignment pattern and the genetic algorithm is used to search through the huge solution space. In the second stage, an exact model is employed to evaluate s best sub-carrier assignment patterns obtained in stage 1 and form the select subset. Finally, the best one of the select subset is the good enough solution that we seek. Via numerous tests, this work demonstrates the efficiency of the proposed algorithm and compares it with those of other heuristic methods.

In this paper, we propose a method to solve the distributed optimal power flow problem and discuss the associated implementation. We have combined this method with a projected Jacobi (PJ) method and a modified parallel block scaled gradient (MPBSG) method possessing decomposition effects. With the decomposition, our method can be parallel processed and is computationally efficient. We have tested our method for distributed OPF problems on numerous power systems. As seen from the simulation results, our method achieved a dramatic speed-up ratio compared with the commercial IMSL subroutines.

In this paper, we propose two techniques to solve the nonlinear constrained optimization problem in large scale mesh-interconnected system. The first one is a diagram-method-based decomposition technique which decomposes the large scale system into some small subsystems. The second technique is a projected-Jacobi-based parallel dual-type method which can solve the optimization problems in the decomposed subsystems efficiently. We have used the proposed algorithm to solve numerous examples of large scale constrained optimization problems in power system. The test results show that the proposed algorithm has computational efficiency with respect to the conventional approach of the centralized Newton method and the state-of-the-art Block-Parallel Newton method.

In previous research, we have proposed a parallel block scaled gradient with decentralized step-size (PBSGDS) method. The method circumvents the difficulty of determining a step-size in the distributed computing environment and enables the proposed parallel algorithm to execute in a distributed computer network with limited amount of date transfer. In this paper, we implement the parallel algorithm within two real Independent System Operator (ISO) Networks, including homogeneous and heterogeneous types PCs-Networks environments, and demonstrate the computational efficiency and numerical satiability through numerous simulation test results in solving a Convex Block Additive Unconstrained (CBAU) optimization problem. Furthermore, the test results show that the performance of the proposed parallel algorithm appears more attractive due to the asynchronous effect in the distributed computing environment.

In this paper, we propose a converting technique based method to solve nonlinear multi-commodity network flow (NMNF) problems with a large number of capacity constraints and discuss the associated implementation. We have combined this method with a successive quadratic programming (SQP) method and a parallel dual-type (PDt) method possessing decomposition effects. We have tested our method in solving a kind of lattice-type network system examples of NMNF problems. The simulation results show that the proposed algorithm is efficient for solving NMNF problems and successfully handles a large number of coupling capacity constraints. Furthermore, the computational efficiency of the proposed algorithm is more significant while the numbers of capacity constraints are increased.